ben article

8/3/2019 Ben Article

1/11

On developing a prospecting tool for wind industry and policydecision support $

Charles McKeown a ,n , Adesoji Adelaja b ,1 , Benjamin Calnin c ,2a The Department of Agricultural Food and Resource Economics (AFRE), Michigan State University, 401 Agriculture Hall, Michigan State University, East Lansing, MI 48823, USAb MSU Land Policy Institute, 401 Agriculture Hall, Michigan State University, East Lansing, MI 48823, USAc MSU Land Policy Institute, 305 Manly Miles Building, 1405 South Harrison Road, Michigan State University, East Lansing, MI 48823, USA

a r t i c l e i n f o

Article history:Received 30 July 2010Accepted 4 November 2010

Keywords:Decision supportRenewable energyWind energyResource assessment

a b s t r a c t

This paper presents the rudiments of a Wind Prospecting Tool designed to inform private and publicdecision makers involved in wind industry development in reducing transaction costs associated withidentifying areas of mutual focus within a state. The multiple layer decision support framework hasproven to be valuable to industry, state government and local decision makers. Information on windresources, land availability, potential land costs, potential NIMBYism concerns and economic develop-ment potential were integrated to develop a framework for decision support. The paper also highlightsimplications for decision support research and the role of higher education in providing anticipatoryscience to enhance private and public choices in economic development.

& 2010 Elsevier Ltd. All rights reserved.

1. Introduction

Interest in renewable energy has grown in the United States inrecent years.The impetus forthisis the growing prospectsfor risingenergy prices, concern about climate change and global warming,and the potential that the US could play a prominent role in therenewable energy industry while enhancing national energysecurity ( US DOE, 2008 ).

Wind energy has received particularly signicant attention.Among the reasons for this are the general abundance of windresources in coastal states and the great plains; the fact that windrepresents a cleaner alternative to coal, natural gas, and nuclearpower; the fact that wind does not involve signicant competitionwith other land-based industries for land; and the perceivedexistence of an opportunity for the US to drive technologicaladvancements that can result in greater competitive or compara-tive advantage ( OConnell, 2007 ). In recent years, the Obama

Administration has sought to promote wind energy through itsinvestments in research and development, especially via theAmerican Recovery and Reinvestment Act of 2009 (ARRA). The billincluded billions of dollars of investment in renewable energythrough the Department of Energys community bloc grants,

industry loans and grants, and assistance to the states. This wasdesigned to deepen the US presence in wind energy technologydevelopment and deployment.

Over time, as fossil fuel based energy has become moreexpensive, wind energy costs have trended downward ( Grosset al., 2003 ). However, despite this downward trend in cost of developmentand lowoperating cost of wind energy, its high initialcost represents a barrier to adoption ( Owen, 2006 ). Other barriersinclude the limited status of the industry in the US, vis-a-vis itspotential; the long duration between the choice of wind energy asan energy source and its development anddeployment; the limitedincentives in place at the state level to spur wind energy devel-opment by increasing consumer, utility company and local adop-tion (DSIRE, 2009); the limited existence of a state regulatory andmanagement frameworks for managingwind energy expressiononthe landscape; and NIMBYism 3 the fact that many homeownersand communities tend to oppose wind turbines for visual, ecolo-

gical (e.g. bird and bat kills) and other reasons ( Alberts, 2005 ).While wind energy is clean and does not generate the usualenvironmental pollution typically associated with coal and otherfossil fuel based energy systems, the challenges involved in itsdeployment remain a major barrier to adoption. The search forinformation and knowledge by policy makers and the industrymakes the developmentof decision support tools essential. For one

Contents lists available at ScienceDirect

journal homepage: www.elsevier.com/locate/enpol

Energy Policy

0301-4215/$- see front matter & 2010 Elsevier Ltd. All rights reserved.

doi: 10.1016/j.enpol.2010.11.015

$ The project was supported by funding the Hannah Professor Research Endow-ment at Michigan State University.

n Corresponding author. Tel.: +1 517 355 4702.E-mail addresses: [email protected] (C. McKeown),

[email protected] (A. Adelaja) , [email protected] (B. Calnin) .1 Tel.: +1 517 355 4702.2 Tel.: +1 517 432 8800x107.

3 Not in My Backyard (NIMBY) is dened here as local residents opposing windenergy developments in close proximity to their residence or place of work or intheir community while not necessarily expressing opposition to the same type of development elsewhere.

Please cite this article as: McKeown, C., et al., On developing a prospecting tool for wind industry and policy decision support. EnergyPolicy (2010), doi: 10.1016/j.enpol.2010.11.015

Energy Policy ] (]]]] ) ]]] ]]]
http://-/?-http://www.elsevier.com/locate/enpolhttp://dx.doi.org/10.1016/j.enpol.2010.11.015mailto:[email protected]:[email protected]:[email protected]://dx.doi.org/10.1016/j.enpol.2010.11.015http://dx.doi.org/10.1016/S0960-1481(98)00006-8http://dx.doi.org/10.1016/S0960-1481(98)00006-8http://dx.doi.org/10.1016/S0960-1481(98)00006-8http://dx.doi.org/10.1016/S0960-1481(98)00006-8http://dx.doi.org/10.1016/S0960-1481(98)00006-8http://dx.doi.org/10.1016/S0960-1481(98)00006-8http://dx.doi.org/10.1016/S0960-1481(98)00006-8http://dx.doi.org/10.1016/S0960-1481(98)00006-8http://dx.doi.org/10.1016/S0960-1481(98)00006-8http://dx.doi.org/10.1016/j.enpol.2010.11.015mailto:[email protected]:[email protected]:[email protected]://dx.doi.org/10.1016/j.enpol.2010.11.015http://www.elsevier.com/locate/enpolhttp://-/?-


2/11

thing, such tools have the potential to reduce barriers to adoptionand development.

State policies are critical to the adoption of wind energy, as hasbeen shown in states such as Texas, Minnesota, California, andColorado ( DSIRE,2010 ). More recently, theprovinceofOntarioCanadahas essentially spurred signicant action in wind energy through theadoption of sweeping policy changes that involves a target of scalingback coal generating capacity ( Ontario Legislature, 2009 ). Under-

standing where wind resources are abundant, where opposition islikely to be lower, where land values are reasonable and where othersupportiveinfrastructureexistscould improve state level planning forenergy development. For example, in Michigan, the state created aWind Resource Zoning Board ( Michigan Senate, 2008 ) to identify thebest regions where enabling policies and proactive transmissionplanning and deployment can be targeted.

Wind developers also need such tools in order to facilitate theirsearch for locations and their attempt to conduct focused communityoutreachandeducation. Perhapsmoreimportantly,developers needtobe able to get a quick read not only onresource availability, but also onvarious environmental, cost and community concern factors. This iswhy decision support tools such as the Land PolicyInstitute (LPI) WindProspectingTool have been of value to theindustry andgovernmentinthe state of Michigan ( Adelaja et al., 2007 ).

The objective of this paper is to present the framework used indeveloping theLPI WindProspectingTool. Thetoolguidedthe workof the Michigan Wind Resource Zoning Board, state agencies andother decision-makers at the local level. It has also been used bymanywind-interestedrmsin site selection. Thestateof Michigan,which has been very active in developing policies to spur windindustry development, is our case study. In the balance of thispaper, a background is provided on the Michigan wind develop-ment environment. This is followed by the description of themethodology used in developing the tool, features of the tool andthe public and private sector implications.

2. Background on Michigan

Despite its unique location and assets, Michigan lags behindmany other states in the nation in installed wind generatingcapacity. According to the National Renewable Energy Laboratory(NREL), while Michigan has the land available and the windresource to install 59,000 MW (plate capacity 4 ) of potentiallydevelopable wind resource capacity onshore when landscapeand wind resource are considered ( Elliott et al., 2010 ), of the thirtyonestates thathave installedsystems,Michigan ranks twenty sixthin installed capacity but fourteenth in resources. Fewer than150 MW have been installedin Michigan ( AWEA, 2010 ), in contrastto Texas whichhas installeda total of 9410 MW of capacity ( AWEA,2010 ). Michigan is very near the bottom in terms of the ratio of installed to potential wind generating capacity, despite its greater

need for energy independence due to its relative isolation as apeninsula state without large resources of coal or other conven-tional fuel for electricity generation.

According to a report by the Renewable Energy Policy Project,the gap in Michigans capacity offers tremendous opportunity forwind energy industry growth ( Sterzinger and Syrcek, 2004 ).Nationwide, states are increasingly pursuing renewable energysources and many with wind energy potential are pursuing suchpotential as a matter of policy. According to Michigans 21stCentury Energy Plan, approximately52009200 GWh of additionalrenewable energy is needed by December 31, 2015 ( Lark, 2007 ).

Wind energy development may offer a new economic opportunityfor Michigan. Renewable energy development has been a boon forsome of the progressive nations in Europe resulting, for example,in the creation of tens of thousands of jobs in both Germany andDenmark ( EWEA, 2009 ).

Michigan currently relies on coal and nuclear fueled base-loadgeneration units for about 83% of its annual electricity production,with the balance coming from natural gas, hydropower, and other

sources. Less than 2% is currently generated from renewable energy(EIA,2010). Allof the fuel feedstock for coal and nuclear areimportedfrom other regions of the US ( MPSC, 2007 ). Annual dollar exports forcoal-based electric energy alone were over $1 billion in 2006 ( MPSC,2007 ). Given the anticipated increase in the prices of fossil fuels,energy security is now a cornerstone of public policy in the state.

The largely untapped wind resource in Michigan could givewind energy an edge, provided that some of the unnecessarybarriers to wind energy development are reduced. The state isseeking to take critical steps not only to identify those barriers, butto strategically reduce them. Barriers to wind development inMichigan appear to be causing existing rms andinvestors to workin other states. These barriers create creating developer riskassociated with inadequate site specic information about windpotential, contract opportunities, connectivity opportunities andpricing strategy.An operational objective of LPIs WindProspectingTool project was therefore, to increase the chance to reduce thetransaction costs associated with deploying wind systems in thestate, thus fostering viable market activities where feasible.

3. Specic objectives and rationale

The Wind Prospecting Tool was developed to assist state andlocal policy decision makers and the wind energy industry inunderstanding where thewindindustry developmentpressurewillbe expressed the most in Michigan, and to help wind energydevelopers shorten the search time for viable locations. Theframework was designed to be an easily understood, integratedresource that can help:

Filter out areas of low potential for wind energy development.Focus efforts of stakeholders on high quality areas, and providecritical analysis of policy gaps in those areas and at the statelevel to enable wind energy development.Target statewide policy and the wind development commu-nitys investment toward those areas that are most conducivefor wind energy development.Help communities understand their own wind developmentpotential.

The development of the tool focused on nding and assessingthe capacity of the best areas for wind development in Michiganand assessing community by community the potential for localdevelopment.It was designedto lter, focus andtarget informationon wind energydevelopmentin Michiganby providing informationon four categories of receptivity factors 5 that the wind energyindustry, and government experts indicated were important:

Geophysical factors those factors inherent to the landscapesuch as topography, lakes and wetlands.

4 PlateCapacityrefersto a manufacturers ratedtotal poweroutputcapacityof awind energy generating system or the sum of these capacities for a wind farm.

Actual output varies widely due to wind resource levels and site design.

5 An attempt was made in this study to identify, as much as possible, all majordecision factors that could affect business wind site location and governmentselection of locations to emphasize appropriate industry development enablingpolicies. All factors considered in this analysis were selected based on the polling if industry and government representatives about relevant factors to consider in

building a value added tool.

C. McKeown et al. / Energy Policy ] (]]]] ) ]]] ]]]2

http://dx.doi.org/10.1016/j.enpol.2010.11.015http://dx.doi.org/10.1016/j.enpol.2010.11.015


3/11

Land/economic factors those factors related to land use,development pressure and local economic conditions.Environmental concerns those factors related to the presenceor absence of endangered species, rare ecological communities,and highly unique habitats.Local policy those factors that relate to planning and zoning 6

and the expression of NIMBYism.

The presence of these factors and their interaction can make a

community an excellent place for wind power development orpresent transaction costs so high as to preclude it entirely.Articulating these factors allows community and state decision-makers to understand andaddress policy and educational efforts toreduce or eliminate those transaction costs.

4. Methods

Wind site selection decisions are essentially site suitabilityexercises. Therefore, an appropriate starting point and modelingframework is a site suitability model that weighs various factors(locationbenetsand barriers) relative to each other based on a setof key site selection criteria. This approach is commonly used inecologic studies to determine the suitability of a particular habitatto an individual species or clades of species ( Ahmadi-Nedushanet al., 2006 ), in land use modeling to predict patterns of develop-ment ( McDonald and Brown, 1984 ), and in the corporate andmunicipal planning process ( Malczewski, 2003 ). This approach,however, has not been widely applied in wind energy industry siteselection, Voivontas et al. have developed a decision support tool,however it does not extend beyond the wind resource and land-scape issues ( Voivontas et al., 2009 ). Given the specic sitecharacteristics thatare necessaryfor a windprojects development,a suitability index modeling framework that includes economicand policy indicators, was determined to be the appropriatemethodology for developing the Wind Prospecting Tool.

Denote the desirable attributes of a given potential winddevelopment project site by the matrix X such that each X ijrepresents the level of the ith attributeat the jth site.The attributesshould include all factors relevant to the developers initial siteselection process, which our expert panel indicated would includethe wind resource capacity, availability of open space, favorabilityof land values, low opposition to wind development and lowpotential for long term encroachment of other land uses. Denotethe aggregate site suitability of the jth location as Y j such that the

suitability function takes the form Y j Pn

i 1g i X ij . Note that a simple

linear aggregate function is assumed where g i 4 0, for all i.

Y j is the aggregation coefcient associated with the jth suitabilityindex and Y is the threshold level that the developer is looking for todetermine that a project site is worth exploring for making appro-priate leasing and site study investments. If Y j Z Y , the developernds the site attractive. However, if Y j o Y the site is unattractive. Asdevelopers and the state seek places to concentrate their efforts, thespatial distribution of Y j becomes important. Of course, Y j variesacross locations andis alsodependenton the inherent X i factors.Notethat other factorsaffect siteselection,but areconsideredin thesecondstep in the site selection process (e.g. information on grid access andnetworking charges) which can only be discovered after a developerzeros in on a potential site and begins the to explore the site specicgrid connectivity and other issues.

Given the sequential nature of the siting process where initialsuitability is determined rst, followed by nal decisions (whichcan take years), we focused our study on the former. The omissionof the availability and proximity of transmission infrastructure isobvious, but our initial focus is initial site selection since thatrepresents the rst step in a complete site assessment. Transmis-sion is obviously a key factor in siting wind energy developmentsand the lack of transmission (interms of presence, interconnectionoptions, and congestion) is one of the primary bottlenecks todevelopment in the US ( US DOE, 2008 ). However, our ability toincorporate transmission informationinto the toolwas constrainedby homeland security concerns which under the USA Patriot Act(US Congress, 2001 ), makes the spatially explicit details abouttransmission infrastructure sensitive information and not forpublication. Furthermore, proximity to transmission is not alwaysthe most relevant factor in connecting to the grid in the MidwestSystems Operator (MISO) region, where the grid is congested inplaces and well under capacity in other areas. Each potentialinterconnection is modeled by both MISO and the transmissioncompany to assess the ability of the grid to absorb the proposedpower production at the applicants proposed location. If there areimprovementsrequired by theapplicant, thecost of those improve-ments is borne by the applicant.

The fact that grid connection studies are carried out in twostages with separate costs for each stage and the uncertainty in thecost of eventual connection and networking makes it virtuallyimpossible to attempt to simultaneously and accurately factor intransmission. Therefore the tool was focused on those early stagedecision factors in wind development. Grid connection studies arerecognized as problems in wind energy development in Michigan,and this recognitioneventually led to the inclusion of provisions inMichigans renewable portfolio standard legislation to help rectifythe situation. That outcome is discussed in greater detail later inthis paper.

To obtaininformationon relevant X categories, we convenedanexpert group consisting of wind developers, electricity regulators,transmission companies and local policy makers empanelled toalso help weighthe relative importance of each factor(the g s). Theprocess consisted of a series of questions to panelists in March of

2007 to weigh the relative importance of each factor and also

Table 1Data used in the wind index and what it represents.

Data Proxy for Possible index score

Wind speed score Wind density for power generation 350 Agricultural land contiguity and area The number of towers that can be strung together in a reasonably compact setting 180Forest land contiguity and area The number of towers that can be strung together in a reasonably compact setting 130Per acre value of agricultural land Land Costs 130Population density: 2000 Possible local resistance to wind farm installation 130

Population density change: 19902000 Pressure for residential and other types of development 80

Total possible 1

6 This is an important consideration in Michigan, where local governments(1856 local governments in all) are responsible for planning and zoning and localplanning and zoning commissions preside over relatively small jurisdictions and in

many cases small populations as well.

C. McKeown et al. / Energy Policy ] (]]]] ) ]]] ]]] 3



4/11

solicited recommendations regarding the addition or deletion of factors. This provided the basis for understanding the industriesdecision making process. Thatpanel identied decision factors anddetermined that the initial decision factors are inherently xedacross places and a weighting technique was needed to identifyrelative weights or g s. Participants were then asked to assignscoresto each g ranging from 1 to1000,bearingin mind therelativevalues of g and the importance in the decision making process. The

g s obtained via this process were the consensus of the panel andthere was strong agreement on the nal weights. These weightswere then presented to other experts for review and feedbackbefore applying them to the analysis. This process yielded con-sensus. The 1000 point index was then constructed using theweights provided by the process described above. The nal indexand the scoring for each factor are shown in Table 1 .

From Table 1 , it is obvious that the wind speed score is a keyfactor in site selection, not only for the public sector, but for theprivate sector as well. This is followed by land availability factorswhich, jointly, account for 310 possible points. Land acquisitioncosts comenext with130 possible points;andpopulation density,aproxy for local resistance at 130 possible points. Future develop-ment pressure and future+other pressures are proxied by popula-tion density change and this factor has a possible score of eightypoints.

Various datasets were used in this analysis, including the USCensus ( US Census Bureau, 2004a, 2004b ) for demographic infor-mation, USGS National Land Use and Land Cover data ( USGS, 2004 )for landscape characteristics, the Michigan State Tax Commis-sion( STC, 2007 ) reports for land valuation, the Michigan Geo-graphic Data Library( CGI, 2009 ) for Community Mapping, andthe fundamental wind resource map used was the NationalRenewable Energy Laboratory fty meter wind density map forMichigan ( AWS Truewind, 2004 ).

5. Index components

5.1. Wind score

Class three or better wind, as modeled by NREL, is generallyconsidered to be the threshold for utility scale wind development(Elliot et al., 1991 ). The wind score is a result of ltering the NREL original 50 m wind density data to produce a map of only classthree to seven wind resources in Michigan. The area in each classwasscaled andadded to produce thenal wind resourcescore foracommunity. Fig. 1 provides a visualization of the distribution of wind resources by location in Michigan. In the map, municipalitieswere scored on the basis of a total of 350 possible points.

5.2. Area of agriculture with wind

Agricultural land has proven to be one of the most importantland types for the installation of wind turbines. Wind turbineinstallation on agricultural land allows a farmer to continuefarming the land because of the minimal footprint of each tower,and the income generated for the farmer by the leases is typicallyfar greater than the minimal loss in capacity to produce cropswhere the turbines are installed. The larger the area of agriculturalland within a community, the greater the amount of towers thatcan be installed within that community. In addition, the ability toassemble coalitions of landowners interested in hosting turbines isincreased. Fig. 2 provides a visualization of the distribution of agricultural land with adequate wind resources by location inMichigan. Note that the highest score for any location in Michiganis 90, which is half of the total points available for the agricultural

land and contiguity score.

0 60 120 18030Miles

0 - 1

2 - 3

4 - 6

7 - 12

13 - 39

40 - 101

102 - 244

Index Score

NREL Wind Class AreasClasses 3 - 7

E

S

N

W

Fig. 1. NREL wind classication and scoring by community in Michigan.

0 - 2

3 - 6

7 - 11

12 - 23

24 - 37

38 - 57

58 - 900 60 120 18030

Miles

Area of Agricultural LandWith Suitable WindDensity at 50m

Index Score

E

S

N

W

Fig. 2. Area of agricultural land by community in Michigan.




5/11

5.3. Contiguity of agricultural land with wind

The cost of an installation and the ease of interconnection arepartiallydecided by the compactness of theentire wind farm. Somecommunities have large areas of agricultural lands that arescattered throughout the landscape while others have agriculturallands that are densely packed. The contiguity measure used isderived from the discipline of landscape ecology. Ot is a directmeasure of how connected or separatedagriculture is in the area inquestion. Scores are determined within each community using theFragstats analysis environment ( McGarigal et al., 2002 ). Fig. 3provides a visualization of the contiguity of agricultural land bylocation in Michigan. Note that thehighest score forany location inMichigan is 90, which is half of the total points available for theagricultural land and contiguity score.

5.4. Area of forest with wind

Forest area with wind is important to wind energy develop-ment, although possibly less desirable than agricultural land forwind development due to siting concerns. This is reected in thelower total possible score of 130 points for forested land. The NREL map models the effect of land cover, including turbulence andobstructions. Therefore, forested areas of Michigan are shown tohave highwind resources, despite landcover related issues. As withagriculture, the more area of forest within a community, the morepotential towersa wind developercan concentrate in an area. Withhigh interconnection costs to the grid, it is important to winddevelopers thatthere be enough installedpower capacityandwindturbines in an area to be able to offset the cost of interconnectioninto the grid. Fig. 4 provides a visualization of the distribution of forest land with adequate wind resources by location in Michigan.

Note that thehighestscorefor anylocationin Michigan is 65 which

is half of the total points available for the forest land andcontiguity score.

5.5. Contiguity of forest land with wind

As with agricultural landscapes, the cost of an installation andthe ease of interconnection are partially decided by the compact-ness of theentire wind farm. Some communitieshave large areas of forest that are scattered throughout the landscape while othershave largely connected tracts. Our contiguity measure was alsoderived from the discipline of landscape ecology as a directmeasure of how connected or separated forest land is in the areain question. Thescores are alsodeterminedwithin eachcommunityusing the Fragstats analysis environment. Forest contiguity withwind is important to wind energy development. The reducedattraction of contiguity of forest land is reected in the lower totalpossible score (as compared to agricultural lands) of 130 points forforested land. Fig. 5 provides a visualization of the contiguity of

forest land with adequate wind resources by location in Michigan.Note that the highestscore forany locationin Michiganis 65,whichis half of the total points available for the forest land andcontiguity score.

5.6. Open space (agriculture and forest) land value

Land value is a fundamental metric in determining lease ratesand local taxes, and also serves as an indicator of other develop-ment pressures. As the value of open landscapes increases, the costof wind instillationsalso increases. Therefore, lowlandvaluesscorehigh on the index. Agricultural value, as dened by the state taxcommission, also includes forest land value. Note that the max-imum scorefor open spacelandvalueis 130points. Fig. 6 providesa

visualization of open space land value distribution in Michigan.

8 - 24

25 - 37

38 - 45

46 - 52

53 - 59

60 - 72

73 - 900 60 120 18030

Miles

Agricultural Land ContiguityWith Suitable WindDensity at 50m

Index Score

E

S

N

W

Fig. 3. Agricultural land contiguity with suitable wind by community in Michigan.

Final Areas

Area of Forest LandWith Suitable 50mWind Density

0 - 1

2 - 3

4 - 6

7 - 11

12 - 20

21 - 40

41 - 650 60 120 18030

Miles

E

S

N

W

Fig. 4. Area of forest land by community in Michigan.




6/11

5.7. Population density: 2000

Populationdensityin this index is used to measure the potentialfor local resistance in a community to wind development. Thehigherthe population density within a community, the more likelythere aregoing to be individualswithconcerns aboutissues such asview shed impingement, ice throw, icker fusion and bird strikes.While population density is an imperfect measure of the develop-

ment of a NIMBY reaction, it has been shown to be an indicator inother controversial land uses such as prisonsiting ( Hoyman, 2006 ),US Federal Emergency Management Administration trailer parksiting ( Davis and Bali, 2008 ) and landlls, incinerators and con-nectional facilitysiting ( Rasmussen,1992 ). Notethatthe maximumscore for population density is 130 points. Fig. 7 provides avisualization of population density distribution in Michigan.

5.8. Population density change: 19902000

Population density change measures a communitys potential forother types of development pressures such as residential or com-mercial development which may be more nancially rewarding thanwind leases. This metric is used to capture the willingness on the partof land holders to enter into long term leases vs. the potential payoff from land sale to other types of development. Communities with lowpopulation growth are given high index scores; all negative valueswere given a score of 100. Note that the maximum score forpopulationdensitychange is 130points. Fig. 8 provides a visualizationof population density change throughout Michigan.

5.9. Zoning score

One of the key factors in determining the suitability of an areafor wind energy development is local zoning laws applicable to

6 - 17

18 - 24

25 - 29

30 - 34

35 - 42

43 - 51

52 - 650 60 120 18030

Miles

Forest Land ContiguityWith Suitable WindDensity at 50m

Index Score

E

S

N

W

Fig. 5. Forest land contiguity by community in Michigan.

83.3 - 103.9

104.0 - 117.3

117.4 - 121.6

121.7 - 123.9

124.0 - 126.1

126.2 - 128.5

128.6 - 130.00 60 120 18030

Miles

Index Score

Value of Open Space

E

S

N

W

Fig. 6. Open space score by community in Michigan.

1 - 56

57 - 86

87 - 106

107 - 118

119 - 125

126 - 128

129 - 1300 60 120 18030

Miles

Index Score

Population Density: 2000

E

S

N

W

Fig. 7. Population density in 2000 by community in Michigan.




7/11

wind turbine and energy development within the community. Areview of the zoning language in Michigan applicable to windpower development was conducted, and the level of barrierpresented by zoning was assessed, ranked and scaled. The nalvalue was then subtracted from the nal score. Unfortunately,zoning scoreshave theonly potentiallynegativevalues as thereareno communities that have passed enabling ordinances that reducebarriers for wind development. Positive score are possible. Alsocommunities with no language pertaining to wind were assigned azoning score of zero. Note that the maximum score for local policyis 130 points which no community actually realizes due to themixed nature of ordinance restrictions. Fig. 9 provides a visualiza-tion of local zoning scores throughout Michigan.

5.10. Total index score

Thescoresdiscussed above were summedinorder toproduce nalstate and community maps. These maps were then intersected withthe NREL class three and up areas to clip out the areas withoutdocumented wind resources. Fig. 10 shows the index score with theinuence of thezoningsubtracted.Though high index scoresaremostoftenfound in coastal communities, it is important to note that therearea numberof inlandcommunities with relativelyhigh index scores.

The highest scoring communities in Michigan (index scores of 500 or better) were then selected and aggregated into the top 12wind utility scale wind development areas in the state. It isimportant to note that the grid and transmission issues have notbeenaddressed in this framework due to homeland security issues.Some of these areas will possibly be later determined to beimpractical due to interconnection and grid capacity issues.

Fig. 11 shows the communities that make up each of the top 12

areas for utility scale development. These areas of Michigan

22.5 - 42.4

42.5 - 64.3

64.4 - 72.0

72.1 - 75.4

75.5 - 77.7

77.8 - 79.2

79.3 - 80.00 60 120 18030

Miles

Index Score

Population DensityChange: 1990 - 2000

E

S

N

W

Fig. 8. Population density change from 1990 to 2000 by community in Michigan.

0 60 120 18030Miles

(-23) - (-26)

(-27) - (-28)

(-29) - (-41)

(-42) - (-46)

(-47) - (-56)

(-57) - (-69)

(-70) - (-100)

Index Score

Zoning Score

E

S

N

W

Fig. 9. Wind zoning index score by community in Michigan.

TotalWith Zoning

59 - 249

250 - 339

340 - 391

392 - 430

431 - 487

488 - 584

585 - 7140 60 120 18030

Miles

Index Score

E

S

N

W

Fig. 10. Wind index score with adjustments for local policy by community in

Michigan.




8/11

representthe areaswhere statepolicieshave become focused sincethe development of the wind prospecting tool. It is no surprise thatthe top 12 areas represented in Fig. 11 are the same areas wherewind generation applications have intensied in Michigan. Thisvery fact highlights the signicant role that the Wind ProspectingTool played in streamlining public and private decision makingwith respect to wind prospecting and policy focus in Michigan.

6. Development scenario results

Perhaps as important as the understanding of high suitabilityareas is the understanding of the potential economic impacts of future development. This is particularity so in Michigan where thestate is in dire need of new job opportunities and where windindustry development is at the forefront of state strategies topromote such opportunities. To provide relevant information on

potential employment and income opportunities, the top 12 areasin the state were further examined to determine the possiblenumber of towers they can accommodate as well as estimatingpower outputs, lease values, maintenance and upkeep jobs as wellas construction job creation.

Wind turbines are generally spaced no closer than ve timestheir rotor diameter ( Patel, 2006 ). Using this guideline, 450 mspacing was determined to be a reasonably conservative estimate

of tower density as it represents a 90 m rotor diameter. The largestturbines commissioned for installation in Michigan have an 80 mrotor diameter. The power possible was calculated by assuming a1.65 MW turbine at 28% capacity factor ( Lark, 2007 ). Job creationwas estimated by employing the Jobs and Economic Impact Model(JEDI) tooldeveloped by theNationalRenewable Energy Laboratory(NREL, 2007). To generate reasonable estimates of impacts, severalscenarios were calculated using 5%, 10%, 15% and 20% of the windresource area. These levels were chosen as reasonable scenarios of developmentpotential andtheyprovide a basisfor comparison andcontrast of the impacts of wind development. The results of thisanalysis at the 15% and 20% levels are shown in the Tables 2 and 3.

7. Community tool

The community level wind tool component includes informationon each Michigan communitys potential for wind development. Thisanalysis involved not only the top 12 areas, but all areas in Michiganwith adequate wind resources. Communities were provided custo-mized printable reports that include information on the communitysLPI wind index score and the potential number of towers, jobs, andrevenue thecommunity canreceive with wind development.Thetoolcomponent also included zoning laws applicable to wind energydevelopment in each community, allowing residents and policymakers within the community to explore whether or not local zoningcanimpact onwind development. Fig. 12 representsa screenshotfromthe larger tool provided on the LPI website for Michigan communitiesto access. The area of the state represented in the map is the thumbarea in the Eastern part of the lower peninsula of Michigan.

8. Additional value added information available through thetool map server

Beyond the factors already addressed above, there is a host of environmentalandlandscapeissues thataffectwind power sitinganddevelopment. With the helpof project partners, this information wasincluded in the tool as well. These factors include areas of criticalhabitat for threatened and endangered species obtained from theMichigan Natural features Inventory (MNFI, 2007), conservation and

0 60 120 18030Miles

Final Areas

Top 12Eastern UP

Keweenaw

Leelanau

Mason County

Presque Isle

Straits

Thumb 1

Thumb 2

Thumb 3

Traverse Bay

UP Ribbon

UP Ribbon 2

E

S

N

W

Fig. 11. The Top 12 contiguous areas for wind development in Michigan.

Table 2Economic and power projections if 15% of the available land area is used in the top 12 locations in Michigan.

Area name Turbines possible Power productionpossible (MW)

Potential land leaserevenue

Potential maintenanceand upkeep jobs

Potentialconstruction jobs

Eastern IP 183 84.7 $366,588 24 372Keweenaw 255 118.0 $510,714 34 518Leelanau 197 91.1 $394,434 26 400Mason County 71 32.7 $141,372 9 143Presque Isle 24 11.2 $48,348 3 49Straits 147 67.7 $293,148 19 297Thumb 1 273 126.2 $546,210 36 554Thumb 2 319 147.6 $638,928 42 648Thumb 3 273 126.2 $546,516 36 555Traverse Bay 150 69.3 $300,186 20 305UP Ribbon 108 49.7 $215,118 14 218UP Ribbon 2 42 19.5 $84,456 6 86




9/11

recreation land holdings obtainedfrom theNature Conservancy ( TNC,2007 ), wetlands obtained from the US National Wetlands Inventory(USGS, 2006 ), inland lakes and rivers obtained from the MichiganCenter from Geographic Information ( CGI, 2009 ) and steep slopesobtained from the US Geological Survey ( USGS, 2005 ).

These areas were not subtracted from the total area available fordevelopment. They serve as indicators that, as part of comprehensivesite assessment, may be some areas of concern. For example, aconservation easement for agriculture may or may not eliminate thepossibility of wind turbine instillation. The location of the easementson the map indicates that this issue must be investigated. Similarly,the presence of an endangered species may eliminate an area orsimply require special construction considerations. With futurefunding, these issues can be examined in more detail. This alsoapplies to the modeling of migratory yways and avian habitat tominimize bird strike potential. An example of potential exclusionaryareas in Manistee County Michigan is shown in Fig. 13 .

9. Summary and conclusions

The LPIs Wind Prospecting Tool represents an innovative

integration of key industryand policy considerations in developing

a decision support framework for wind industry development.While the analysis might appear simple and straightforward, ithighlights the importance of decision support tools to guide thechoice of synergistic opportunities for industry and government towork together. While this tool was developed specically forMichigan, it has been of interest to companies and policy makersin many other states. It has also been the subject of keen interest bystate ofcials in Michigan and beyond who are exploring howuniversity research can help in public policy, especially in areaswhere industry and government goals are not necessarily inconict. Zeroing in on the sweet spots where opportunities exist,while avoiding prescriptive value judgments in policy research isan area of important need with respect to university research,especially during these times when the private and public sectorsare seeking new opportunities to streamline public and privatechoices that are meaningful in economic development.

From the analysis above, it is obvious that the range of considerations involved in informing a public policy challenge isvast, if not outright complicated. Unfortunately, however, oppor-tunities for constructive dialogue that will lead to valuable frame-works for moving forward are limited, especially when the issue of concern involves multiple companies, multiple consumer interests

and concerns, and sometimes overlapping and conicting public

Table 3Economic and power projections if 20% of the available land area is used in the top 12 locations in Michigan.

Area name Turbines possible Power productionpossible

Potential land lease value

Potential maintenanceand upkeep jobs

Potentialconstruction jobs

Eastern IP 240 553.5 $479,200 32 486Keweenaw 334 771.1 $667,600 44 677Leelanau 258 595.5 $515,600 34 523Mason County 92 213.4 $184,800 12 188

Presque Isle 32 73.0 $63,200 4 64Straits 192 442.6 $383,200 25 389Thumb 1 357 824.7 $714,000 47 725Thumb 2 418 964.7 $835,200 55 848Thumb 3 357 825.1 $714,400 47 725Traverse Bay 196 453.2 $392,400 26 398UP Ribbon 141 324.8 $281,200 19 285UP Ribbon 2 55 127.5 $110,400 7 112

Fig. 12. Screenshot of the community wind tool web interface for Michigan.




10/11

policy objectives. While this paper stopsshort of presenting detailsabout how Michigan leveraged this tool to create winwin solu-tions, the value of the tool has been signicant. For example theMichigan Wind Resources Zoning Board, which was mandated bythe Michigans Clean Renewable and Energy Efciency Act of 2007,contracted with the LPI to use the tool framework to help the statedelineate areas of priority for wind energy development andtransmission planning as specied in that legislation.

To date, though two contracts, the LPI provided such service tothe state of Michigan ( PSC, LPI, 2009). These decision supportactivities played a critical role in the assessment of wind inMichigan, in the analysis of the transmission grids ability tointegrate wind energy, and eventually in the adoption of acooperative model for designing and funding upgrades to trans-mission infrastructure to allow wind energy a faster path to gridintegration. The tool has also been leveraged by several interna-tional, national, and state based companies in the process of prospecting for wind development projects in Michigan.

Decision support tools are valuable only when forward lookingchoices are made to anticipate and provide, ahead of time,information and analysis that can add value. Such information istypically characterized by the integration of multiple analysistechniques and disciplines, including spatial analysis, economicanalysis, policy analysis and the polling of policy makers andindustry decision makers. This raises a challenge to researches thatare neither close enough to government or industry to understandtheneeds of either. Obviously, synthesis is important in generatingeffective tools that add value. Perhaps more importantly, a multi-disciplinaryapproach is required, whichcan often be a challenge inthesetting of highereducation. Other studies of a similar naturebythe authors include the Michigan Offshore Wind Resource Assess-

ment, Michigans Browneld and Renewable Energy Resource

Assessment, and LPIs analysis of Renewable Portfolio Standardsand their inherent features in the development of renewableenergy. All three led to signicant policy changes and industrycoalescence around catalytic industry development efforts inrenewable energy.

The analyses presented above also involved signicant out-reach, including the creation of a web based tool as the primarydelivery mechanism. This is made possible by the integration of research, targeted outreach and technology. This raises the ques-tion of whether or not theprivatesector shoulddevelopthis type of decision support. It probably should if the tool is valuable only tothe private sector. However, on potentially contentious issues suchas wind development, the benets of thesetypes of tools arelargelyto the public sector. This in turn raises the question of whether ornotadequate resources will be provided aheadof time by the publicsector, especially in an environment where tools developmentmust involve the anticipation of public policy needs in order forsuch tools to be valuable within the time frame in which publicchoices are made.

References

Adelaja, S., et al., 2007. The Land Policy Institute Wind Prospecting Tool Prototype.Land Policy Institute, Michigan State University, East Lansing, Michigan.

Ahmadi-Nedushan, B., St-Hilaire, A., Be rube, M., Robichaud, E., Thiemonge, N.,Bobe e, B., 2006. A review of statistical methods for the evaluation of aquatichabitat suitability for instream ow assessment. River Research and Applica-tions 22, 503523. doi: 10.1002/rra.918 .

Alberts, D.J., 2005. Delphi Inquires Into Wind Siting Issues, Report to the MichiganEnergy Ofce, 2005, Lawrence Technical University, MI.

American Wind Energy Association, 2010. Year End 2009 Market Report. American

Wind Energy Association, Washington, DC.

Fig. 13. Map showing one county with potential exclusionary areas for ecological reasons.


http://dx.doi.org/10.1002/rra.918http://dx.doi.org/10.1016/j.enpol.2010.11.015http://dx.doi.org/10.1016/j.enpol.2010.11.015http://dx.doi.org/10.1002/rra.918


11/11

AWS Truewind, 2004. Wind Speed of Michigan at 70 meters. / http://www.michigan.gov/documents/dleg/MI_FinalSPD70m_09_09_04_238941_7.pdf S(accessed October 1, 2008).

Database of State Incentives for Renewables and Energy Efciency, 2010, DSIRE.Available at: / www.dsire.org S (accessed March 25, 2010).

Davis,B.C., Bali,V.A.,2008. Examiningthe role ofrace,NIMBY, andlocalpolitics inFEMAtrailer park placement. Social Science Quarterly 89, 11751194. doi: 10.1111/ j.1540-6237.2008.00563.x .

Elliot, D.L., Wendell, L.L., Gower, G.L, 1991. An Assessment of the Available WindyLand Area and Wind Energy Potential in the Contiguous United States. NationalRenewable Energy Laboratory, Golden Colorado.

Elliott, D.L., Schwartz, Marc, Haymes, Steve, Heimiller, Donna, Scott, George,Flowers, Larry, Brower, Michael, Hale, Erik, Phelps, Bryon, 2010. 80 m and100 m Wind Resource Map for the United States. National Renewable EnergyLaboratory, Golden Colorado AWS Truewind, Albany NY.

European Wind EnergyAssociation(EWEA), 2009. Wind at Work: Wind Energyand Job Creation in the EU. European Wind Energy Association, Brussels, Belgium.

Robert, Gross, Leach, Matthew, Bauen, Ausilio, 2003. Progress in renewable energy.Environment International 29 (1). doi: 10.1016/S0160-4120(02)00130-7 ISSN0160-4120.

Hoyman, M., Weinberg, M., 2006. The process of policy innovation: prison sitingsin rural North Carolina. Policy Studies Journal 34, 95112. doi: 10.1111/ j.1541-0072.2006.00147.x .

Lark, P., 2007. Michigans 21st Century Energy Plan, Michigan Public ServiceCommission Report, Lansing Michigan.

Malczewski, J., 2003. GIS-based land-use suitability analysis: a critical overview.Progress in Planning 62 (1), 365. doi: 10.1016/j.progress.2003.09.002 July 2004, ISSN 0305-9006.

McDonald, G.T., Brown, A.L., 1984. The land suitability approach to strategic land-

use planning in urban fringe areas. Landscape Planning 11 (2),125150. doi: 10.1016/0304-3924(84)90035-2 July, ISSN 0304-3924.McGarigal, K., Cushman, S.A., Neel, M.C., Ene, E. 2002. FRAGSTATS: Spatial Pattern

Analysis Program for Categorical Maps. Computer software program producedby the authors at the University of Massachusetts, Amherst. Accessed at:/ http://www.umass.edu/landeco/research/fragstats/fragstats.html S .

Michigan Center for Geographic Information (CGI), 2009. Michigan GeographicFramework: State of Michigan. Version 9b. Michigan Center for GeographicInformation, Lansing, MI.

Michigan Senate, 2008. The Clean, Renewable, and Efcient Energy Act (Public Act295 of 2008). Michigan Legislature, Lansing Michigan.

Michigan State Tax Commission (STC), 2007. Ad Valorem Property Tax Reports.Michigan Department of Treasury, Lansing MI.

Michigan Public Service Commission, (MPSC), 2007. Fuel Mix Disclosure Data.Lansing Michigan. Accessed at: / http://www.dleg.state.mi.us/mpsc/electric/download/fuelmix.pdf S .

OConnell,R., Pletka, R.,2007. Percent WindEnergy Penetrationin theUnited States:A Technical Analysis of the Energy Resource. Black and Veatch, Overland Park,KS.

Ontario Legislature, 2009. Green Energy Act, Ontario Legislature, Bill 150, TorontoCanada.

Owen, A., 2006. Renewable energy: externality costs as market barriers. EnergyPolicy 34(5), Hong Kong Editorial Board meeting presentations, March 2006,ISSN 0301-4215, 10.1016/j.enpol.2005.11.017.

Patel,M., 2006. Wind andSolarPowerSystems, Design, Analysisand Operation. CRCPress, Boca Raton, Florida.

Public Sector Consultants, Land Policy Institute, 2009. Final Report of the MichiganWind Energy Resource Zone Board, Lansing Michigan. Available at: / http://www.dleg.state.mi.us/mpsc/renewables/windboard/werzb_nal_report.pdf S .

Rasmussen, Thomas H., 1992. Not in my backyard: the politics of siting prisons,landlls, and incinerators. State & Local Government Review 24 (3),128134 Autumn.

Sterzinger, G., Syrcek, M., 2004. Wind Turbine Development: Location of Manu-facturing Activity, Renewable Energy Policy Project, Washington, DC.

TheNatureConservancy(TNC),2007.The Conservationand RecreationLands Database./ http://www.nature.org/wherewework/northamerica/states/michigan/science/art22873.html S (accessed June 15th, 2007).

US Congress, 2001. Uniting and Strengthening America by Providing AppropriateTools Required to Intercept and Obstruct Terrorism (USA Patriot) Act of 2001.P.L. 107-56.

US Census Bureau, 2004a, US Census of Population and Housing, Washington, DC,2004.

US Census Bureau, 2004b, US Population Projections, Washington, DC, 2004.US Departmentof Energy, EnergyEfciency and Renewable Energy, 2008.20% Wind

Energyby 2030:Increasing WindEnergysContributionto US ElectricitySupply.US Department of Energy, Washington, DC.

US Geological Survey (USGS), 2004. National Land Use and Land Cover Dataset, USGeological Survey, Washington, DC.

US Geological Survey (USGS), 2006.The National WetlandsInventory, US GeologicalSurvey, Washington, DC.

US Geological Survey (USGS), 2005. The National Elevation Dataset, US GeologicalSurvey, Washington, DC.

US Energy Information Administration (EIA), 2010. State Electricity Proles,Washington DC. Accessed at: / http://www.eia.doe.gov/cneaf/electricity/st_proles/sep2008.pdf S .

Voivontas, D., Assimacopoulos, A., Mourelatos, J.Corominas, 2009. Evaluation of renewable energy potential using a GIS decision support system. RenewableEnergy 13 (3), 333344. doi: 10.1016/S0960-1481(98)00006-8 March 1998,ISSN 0960-1481.


Please cite this article as: McKeown, C., et al., On developing a prospecting tool for wind industry and policy decision support. Energy
http://www.michigan.gov/documents/dleg/MI_FinalSPD70m_09_09_04_238941_7.pdfhttp://www.michigan.gov/documents/dleg/MI_FinalSPD70m_09_09_04_238941_7.pdfhttp://www.dsire.org/http://dx.doi.org/10.1111/j.1540-6237.2008.00563.xhttp://dx.doi.org/10.1111/j.1540-6237.2008.00563.xhttp://dx.doi.org/10.1016/S0160-4120(02)00130-7http://dx.doi.org/10.1111/j.1541-0072.2006.00147.xhttp://dx.doi.org/10.1111/j.1541-0072.2006.00147.xhttp://dx.doi.org/10.1016/j.progress.2003.09.002http://dx.doi.org/10.1016/0304-3924(84)90035-2http://www.umass.edu/landeco/research/fragstats/fragstats.htmlhttp://www.dleg.state.mi.us/mpsc/electric/download/fuelmix.pdfhttp://www.dleg.state.mi.us/mpsc/electric/download/fuelmix.pdfhttp://www.dleg.state.mi.us/mpsc/renewables/windboard/werzb_final_report.pdfhttp://www.dleg.state.mi.us/mpsc/renewables/windboard/werzb_final_report.pdfhttp://www.nature.org/wherewework/northamerica/states/michigan/science/art22873.htmlhttp://www.nature.org/wherewework/northamerica/states/michigan/science/art22873.htmlhttp://www.eia.doe.gov/cneaf/electricity/st_profiles/sep2008.pdfhttp://www.eia.doe.gov/cneaf/electricity/st_profiles/sep2008.pdfhttp://dx.doi.org/10.1016/S0960-1481(98)00006-8http://dx.doi.org/10.1016/S0960-1481(98)00006-8http://www.eia.doe.gov/cneaf/electricity/st_profiles/sep2008.pdfhttp://www.eia.doe.gov/cneaf/electricity/st_profiles/sep2008.pdfhttp://www.nature.org/wherewework/northamerica/states/michigan/science/art22873.htmlhttp://www.nature.org/wherewework/northamerica/states/michigan/science/art22873.htmlhttp://www.dleg.state.mi.us/mpsc/renewables/windboard/werzb_final_report.pdfhttp://www.dleg.state.mi.us/mpsc/renewables/windboard/werzb_final_report.pdfhttp://www.dleg.state.mi.us/mpsc/electric/download/fuelmix.pdfhttp://www.dleg.state.mi.us/mpsc/electric/download/fuelmix.pdfhttp://www.umass.edu/landeco/research/fragstats/fragstats.htmlhttp://dx.doi.org/10.1016/0304-3924(84)90035-2http://dx.doi.org/10.1016/j.progress.2003.09.002http://dx.doi.org/10.1111/j.1541-0072.2006.00147.xhttp://dx.doi.org/10.1111/j.1541-0072.2006.00147.xhttp://dx.doi.org/10.1016/S0160-4120(02)00130-7http://dx.doi.org/10.1111/j.1540-6237.2008.00563.xhttp://dx.doi.org/10.1111/j.1540-6237.2008.00563.xhttp://www.dsire.org/http://www.dsire.org/http://www.dsire.org/http://www.dsire.org/http://www.michigan.gov/documents/dleg/MI_FinalSPD70m_09_09_04_238941_7.pdfhttp://www.michigan.gov/documents/dleg/MI_FinalSPD70m_09_09_04_238941_7.pdf

ben article

Documents